Building A Business Case For Data Quality: Create The Business Case

Finally, you need to create a business case and present the finding of the data quality checkup. There are two levels of presentation that typically take place after the data quality assessment. The first is a technical presentation to IT giving all the details of completeness, conformity, consistency, accuracy, duplication, and integrity characteristics of the data. IT needs to understand the types of issues in order to figure out what needs to be repaired and have an idea what can be fixed and what it might cost.

The more important presentation is what impact these issues are having on the business. Does the lack of accuracy in the data affect the accuracy of business decisions? How does the completeness of the data affect insurance ratings, loan applications, or well drilling decisions? Are your customer’s committing a crime?

It is a federal crime to willfully and deliberately provide incomplete or incorrect information on loan applications made to federal credit unions or state chartered credit unions insured by NCUA.

How does duplication of records affect marketing campaigns or patient care? How does conformity of phone numbers affect the call center?

Mail Campaign

Printing

$0.50

Campaign Setup

$250.00

List (if Purchased)

0

Postage

$0.44

Number of Pieces

1,000,000

Total

$940,250.00

Response

% of Responders

3%

Total Responders

30,000

% of Conversions

25%

Total Conversions

7,500

Average Sale Price

$150

Total Revenue Per Campaign

$1,125,000

Average Profit per Conversion

$45

Total Profit on Sales

$337,500

Improvements

Duplicate Records

3%

Eliminate Duplicate Costs

$28,200

Bad Addresses

5%

Increase Conversions

$67,500

Number of Mailings per Year

18

Total Impact

$1,722,600

ROI

Increase in Revenue

$782,350

Costs of Implementing Data Quality Solution

$350,000

One-Year ROI

123.53%

Three-Year ROI

564.87%

This table is a simple ROI calculation for a marketing database that has poor data quality. By cleaning up the database, you can eliminate duplicate customer records and correct non-deliverable addresses. In business terms, you can lower your campaign costs while improving your take rates.

Let’s look at another example. You are a telemarketing department that purchased a call list from an outside source. You are chartered with selling a new digital camera for $125. Your average talk time should be under five minutes. This gives each representative an average of 12 calls per hour.

The data has problems that were not readily apparent at the beginning of the campaign. You load the file into the dialer and begin talking to prospects. At the end of the week, you notice that the average talk time has increased to six minutes per call. Because of this increase, your dialer is automatically dropping and redialing the same prospect because there was not an agent available to take the call. When the agent does begin talking to that prospect, he or she is mad at the multiple calls and not interested in listening to the agent’s pitch.

You decide to contact Informatica to perform a data quality assessment on the purchased data. The initial findings discover the address data was incomplete on 15 percent of the records. The name fields were null 3 percent of the time. The phone numbers were inaccurate or empty in 10 percent of the records.

Working with the call center, this checkup confirmed an initial investigation by management. Because the data was inaccurate, the agents had to spend more time correcting the records, which led to the increased talk times. Also, because of the longer talk times, the dropped and redialed calls were causing lower acceptance rates on the connected calls. So not only were the calls handled by the reps per hour dropping, but the offer acceptance percentage per 100 calls was also dropping: a double whammy on profitability.

The hidden cost was the missing and inaccurate phone numbers. When the dialer hit those records, it just flagged them and did not attempt the call. This was an increase of 10 percent in the acquisition cost of the call list, which never would have been detected without the Data Quality Assessment by Informatica Corporation?

These two examples show the bottom up approach and the top down approach discussed in an earlier blog.

“Remember what Forrester Research said in one of it’s reports: ‘Data-quality professionals get shot down because trusted data will not directly increase revenue, reduce costs or improve operational efficiencies, key criteria for getting funding approved.’ ” You must put improving data quality into terms that business will understand and demand. You are not going to improve data quality; you are going to improve their key success indicators, the one’s that their bonus is based on.

This should enable you to put together a successful business case for a data quality initiative or to have it funded as part of a data project.

Ed Lindsey has authored a book on data profiling techniques, Three- Dimensional Analysis: Data Profiling Techniques, that can be found on www.dataprofilingtechniques.comor www.amazom.com for more information on Building the Business Case for data quality.